One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning-Reference-Cited by-同舟云学术

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Jingtao¹^ORCID,Wang Xinyu²^ORCID,Wang Shaoyu³,Zhao Hengwei¹^ORCID,Zhong Yanfei¹^ORCID

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Hubei Provincial Engineering Research Center of Natural Resources Remote Sensing Monitoring, Wuhan University, Wuhan, China

2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

3. College of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10506667.pdf?arnumber=10506667

Reference66 articles.

1. Deep Low-Rank Prior for Hyperspectral Anomaly Detection

2. LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection

3. Spectral–Spatial Feature Extraction for Hyperspectral Anomaly Detection

4. A tutorial overview of anomaly detection in hyperspectral images

5. A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery

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